ESTIMULANTES EN LA GERMINACIÓN Y BIOMETRÍA INICIAL DE DOS VARIEDADES DE MAÍZ MORADO (Zea mays L.)

1 Setup

Instalar version en desarrollo.

if (!require("remotes"))
  install.packages("remotes")
remotes::install_github("flavjack/inti")
library(emmeans)
library(corrplot)
library(multcomp)
source('https://inkaverse.com/setup.r')

cat("Project: ", getwd())
Project:  C:/Users/floza/git/prochira_maiz_morado
session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14 ucrt)
 os       Windows 11 x64 (build 22631)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  Spanish_Latin America.utf8
 ctype    Spanish_Latin America.utf8
 tz       America/Lima
 date     2024-07-29
 pandoc   3.1.11 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version  date (UTC) lib source
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 httpuv          1.6.15   2024-03-26 [1] CRAN (R 4.4.0)
 httr            1.4.7    2023-08-15 [1] CRAN (R 4.4.0)
 huito         * 0.2.4    2023-10-25 [1] CRAN (R 4.4.0)
 inti          * 0.6.5    2024-07-29 [1] Github (flavjack/inti@38be898)
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 knitr         * 1.48     2024-07-07 [1] CRAN (R 4.4.1)
 later           1.3.2    2023-12-06 [1] CRAN (R 4.4.0)
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 mime            0.12     2021-09-28 [1] CRAN (R 4.4.0)
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 mnormt          2.1.1    2022-09-26 [1] CRAN (R 4.4.0)
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 multcompView    0.1-10   2024-03-08 [1] CRAN (R 4.4.0)
 munsell         0.5.1    2024-04-01 [1] CRAN (R 4.4.0)
 mvtnorm       * 1.2-5    2024-05-21 [1] CRAN (R 4.4.0)
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 [1] C:/Users/floza/AppData/Local/R/win-library/4.4
 [2] C:/Program Files/R/R-4.4.1/library

──────────────────────────────────────────────────────────────────────────────

2 Refrencias

  • (PCA) https://www.r-bloggers.com/2017/07/pca-course-using-factominer/
  • (PCA) https://www.youtube.com/watch?v=Uhw-1NilmAk&ab_channel=Fran%C3%A7oisHusson
  • (HCPC) https://youtu.be/EJqYTDTJJug

3 Import data

https://docs.google.com/spreadsheets/d/1E_l9uV3MT1qlJuVtWK66NgevqPH6fVJCekqNhS_VGm0/edit?gid=1893553741#gid=1893553741

url <- "https://docs.google.com/spreadsheets/d/1E_l9uV3MT1qlJuVtWK66NgevqPH6fVJCekqNhS_VGm0/edit?gid=1893553741#gid=1893553741"

gs <- url %>% 
  as_sheets_id()

imbibition <- gs %>% 
  range_read("imbibition") %>% 
  rename_with(~ tolower(.)) %>% 
  mutate(time = tiempo, .after = tiempo) %>% 
  mutate(variedad = case_when(
    variedad %in% c("criollo") ~ "Creole"
    , variedad %in% c("Hibrido") ~ "Hybrid"
  )) %>% 
  mutate(across(1:tiempo, ~ as.factor(.))) 

str(imbibition)
## tibble [2,100 × 7] (S3: tbl_df/tbl/data.frame)
##  $ bloque     : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
##  $ trat       : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ tratamiento: Factor w/ 7 levels "Agua Destilada",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ variedad   : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
##  $ tiempo     : Factor w/ 5 levels "0","3","6","9",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ time       : num [1:2100] 0 0 0 0 0 0 0 0 0 0 ...
##  $ peso       : num [1:2100] 0.58 0.62 0.73 0.72 0.72 0.68 0.71 0.61 0.69 0.64 ...

germination <- gs %>% 
  range_read("germination") %>% 
  rename_with(~ tolower(.)) %>% 
  mutate(variedad = case_when(
    variedad %in% c("criollo") ~ "Creole"
    , variedad %in% c("Hibrido") ~ "Hybrid"
  )) %>% 
  mutate(trat = case_when(
    tratamiento %in% "Agua Destilada" ~ "T0"
    , tratamiento %in% "Algas Marinas 1 L/cil" ~ "T1"
    , tratamiento %in% "Algas Marinas 1,5 L/cil" ~ "T2"
    , tratamiento %in% "Azufre 100 gr.200 L-1" ~ "T3"
    , tratamiento %in% "Azufre 150 gr.200 L-1" ~ "T4"
    , tratamiento %in% "Suero de leche 10%" ~ "T5"
    , tratamiento %in% "Suero de leche 30%" ~ "T6"
  ), .before = tratamiento) %>% 
  mutate(across(1:variedad, ~ as.factor(.))) 

str(germination)
## tibble [42 × 11] (S3: tbl_df/tbl/data.frame)
##  $ bloque     : Factor w/ 3 levels "1","2","3": 1 2 3 1 2 3 1 2 3 1 ...
##  $ trat       : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 2 2 2 3 3 3 4 ...
##  $ tratamiento: Factor w/ 7 levels "Agua Destilada",..: 1 1 1 2 2 2 3 3 3 4 ...
##  $ variedad   : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dia 1      : num [1:42] 2 4 3 0 1 1 0 0 1 0 ...
##  $ dia 2      : num [1:42] 5 4 5 3 2 1 1 4 5 1 ...
##  $ dia 3      : num [1:42] 1 1 1 1 0 0 0 0 0 0 ...
##  $ total      : num [1:42] 8 9 9 4 3 2 1 4 6 1 ...
##  $ pg         : num [1:42] 80 90 90 40 30 20 10 40 60 10 ...
##  $ vg         : num [1:42] 2.67 3 3 2 1.5 ...
##  $ ig         : num [1:42] 2.4 2.7 2.7 0.8 0.6 0.4 0.1 0.4 1.2 0.1 ...

plantula <- gs %>% 
  range_read("plantula") %>% 
  rename_with(~ tolower(.)) %>% 
  mutate(variedad = case_when(
    variedad %in% c("criollo") ~ "Creole"
    , variedad %in% c("hibrido") ~ "Hybrid"
  )) %>% 
  mutate(across(1:variedad, ~ as.factor(.)))

str(plantula)
## tibble [210 × 16] (S3: tbl_df/tbl/data.frame)
##  $ trat           : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ tratamiento    : Factor w/ 7 levels "Agua Destilada",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ variedad       : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
##  $ raiz_lgtd      : num [1:210] 11 8 12 11 8 13 10 12 9 13 ...
##  $ gsr_raiz       : num [1:210] 1.3 1.19 1.51 1.21 1.17 1.13 1.68 1.27 1.03 1.16 ...
##  $ num_raiz       : num [1:210] 8 11 11 9 12 16 10 9 16 11 ...
##  $ peso_fres_raiz : num [1:210] 4.82 3.21 4.91 4.42 4.62 6.07 4.97 6.13 3.05 4 ...
##  $ peso_seco_raiz : num [1:210] 0.73 0.41 0.62 0.66 0.72 0.54 0.75 0.56 0.57 0.74 ...
##  $ alt_planta     : num [1:210] 30 26 28 32 25 27 28 35 29 29 ...
##  $ gsr_tallo      : num [1:210] 5.86 4.56 6.59 4.63 4.55 4.14 4.02 4.32 3.45 3.61 ...
##  $ nhp_hoja       : num [1:210] 5 5 5 6 4 5 5 5 5 5 ...
##  $ larg_hoja      : num [1:210] 26 23 21 27 29 22 24 30 25 23 ...
##  $ grs_hoja       : num [1:210] 0.94 1.15 0.89 0.98 1.01 0.72 0.62 1.03 0.71 1.34 ...
##  $ anch_hoja      : num [1:210] 19.3 19.9 21.5 17.3 18.9 ...
##  $ peso_fres_brote: num [1:210] 5.34 5.99 5.45 4.81 7.03 6.79 4.99 4.53 3.56 4 ...
##  $ peso_seco_brote: num [1:210] 0.5 0.49 1.04 0.78 0.68 0.67 0.69 0.78 0.73 0.75 ...

4 Tratamientos

imbibition %>% 
  group_by(trat, tratamiento) %>% 
  summarise(n = n()) %>% 
  select(!n)
## # A tibble: 7 × 2
## # Groups:   trat [7]
##   trat  tratamiento            
##   <fct> <fct>                  
## 1 T0    Agua Destilada         
## 2 T1    Algas Marinas 1 L/cil  
## 3 T2    Algas Marinas 1,5 L/cil
## 4 T3    Azufre 100 gr.200 L-1  
## 5 T4    Azufre 150 gr.200 L-1  
## 6 T5    Suero de leche 10%     
## 7 T6    Suero de leche 30%

5 Data summary

sm <- imbibition %>% 
  group_by(tratamiento, variedad, tiempo) %>% 
  summarise(across(peso, ~ sum(!is.na(.))))

sm
## # A tibble: 70 × 4
## # Groups:   tratamiento, variedad [14]
##    tratamiento    variedad tiempo  peso
##    <fct>          <fct>    <fct>  <int>
##  1 Agua Destilada Creole   0         30
##  2 Agua Destilada Creole   3         30
##  3 Agua Destilada Creole   6         30
##  4 Agua Destilada Creole   9         30
##  5 Agua Destilada Creole   12        30
##  6 Agua Destilada Hybrid   0         30
##  7 Agua Destilada Hybrid   3         30
##  8 Agua Destilada Hybrid   6         30
##  9 Agua Destilada Hybrid   9         30
## 10 Agua Destilada Hybrid   12        30
## # ℹ 60 more rows

sm <- germination %>% 
  group_by(tratamiento, variedad) %>% 
  summarise(across(pg:ig, ~ sum(!is.na(.))))

sm
## # A tibble: 14 × 5
## # Groups:   tratamiento [7]
##    tratamiento             variedad    pg    vg    ig
##    <fct>                   <fct>    <int> <int> <int>
##  1 Agua Destilada          Creole       3     3     3
##  2 Agua Destilada          Hybrid       3     3     3
##  3 Algas Marinas 1 L/cil   Creole       3     3     3
##  4 Algas Marinas 1 L/cil   Hybrid       3     3     3
##  5 Algas Marinas 1,5 L/cil Creole       3     3     3
##  6 Algas Marinas 1,5 L/cil Hybrid       3     3     3
##  7 Azufre 100 gr.200 L-1   Creole       3     3     3
##  8 Azufre 100 gr.200 L-1   Hybrid       3     3     3
##  9 Azufre 150 gr.200 L-1   Creole       3     3     3
## 10 Azufre 150 gr.200 L-1   Hybrid       3     3     3
## 11 Suero de leche 10%      Creole       3     3     3
## 12 Suero de leche 10%      Hybrid       3     3     3
## 13 Suero de leche 30%      Creole       3     3     3
## 14 Suero de leche 30%      Hybrid       3     3     3

sm <- plantula %>% 
  group_by(tratamiento, variedad) %>% 
  summarise(across(where(is.numeric), ~ sum(!is.na(.))))

sm
## # A tibble: 14 × 15
## # Groups:   tratamiento [7]
##    tratamiento             variedad raiz_lgtd gsr_raiz num_raiz peso_fres_raiz
##    <fct>                   <fct>        <int>    <int>    <int>          <int>
##  1 Agua Destilada          Creole          15       15       15             15
##  2 Agua Destilada          Hybrid          15       15       15             15
##  3 Algas Marinas 1 L/cil   Creole          15       15       15             15
##  4 Algas Marinas 1 L/cil   Hybrid          15       15       15             15
##  5 Algas Marinas 1,5 L/cil Creole          15       15       15             15
##  6 Algas Marinas 1,5 L/cil Hybrid          15       15       15             15
##  7 Azufre 100 gr.200 L-1   Creole          15       15       15             15
##  8 Azufre 100 gr.200 L-1   Hybrid          15       15       15             15
##  9 Azufre 150 gr.200 L-1   Creole          15       15       15             15
## 10 Azufre 150 gr.200 L-1   Hybrid          15       15       15             15
## 11 Suero de leche 10%      Creole          15       15       15             15
## 12 Suero de leche 10%      Hybrid          15       15       15             15
## 13 Suero de leche 30%      Creole          15       15       15             15
## 14 Suero de leche 30%      Hybrid          15       15       15             15
## # ℹ 9 more variables: peso_seco_raiz <int>, alt_planta <int>, gsr_tallo <int>,
## #   nhp_hoja <int>, larg_hoja <int>, grs_hoja <int>, anch_hoja <int>,
## #   peso_fres_brote <int>, peso_seco_brote <int>

6 Objetivos

  1. Evaluar los parámetros de germinación de dos variedades de semillas de maiz morado usando bioestimulante orgánico.

  2. Identificar el mejor tratamiento que influye positivamente en el crecimiento y desarrollo de plantulas en el cultivo de Maíz morado.

6.1 Objetivo Específico 1

Evaluar los parámetros de germinación de dos variedades de semillas de maiz morado usando bioestimulante orgánico.

  • Imbibiciación, % germinación, velocidad e IG

6.1.1 Imbibición

trait <- "peso"
fb <- imbibition

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad +  (1 + tiempo|tratamiento)") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + tiempo +  trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers %>% kable()
index bloque trat variedad tiempo tratamiento peso resi res_MAD rawp.BHStud adjp bholm out_flag

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: peso
##                 Df  Sum Sq Mean Sq  F value              Pr(>F)    
## bloque           2  0.0021 0.00105   0.1222               0.885    
## tiempo           4 10.0058 2.50146 289.7715 <0.0000000000000002 ***
## trat             6  3.2174 0.53624  62.1186 <0.0000000000000002 ***
## variedad         1  0.6165 0.61649  71.4150 <0.0000000000000002 ***
## trat:variedad    6  2.6467 0.44111  51.0987 <0.0000000000000002 ***
## Residuals     2080 17.9556 0.00863                                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ tiempo|variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
tiempo variedad trat emmean SE df lower.CL upper.CL group
1 12 Creole T0 0.8296986 0.0086019 2080 0.8128293 0.8465678 a
3 9 Creole T0 0.8279129 0.0086019 2080 0.8110436 0.8447821 a
2 3 Creole T0 0.7645081 0.0086019 2080 0.7476388 0.7813774 b
4 6 Creole T0 0.7620295 0.0086019 2080 0.7451603 0.7788988 b
5 0 Creole T0 0.6398176 0.0086019 2080 0.6229483 0.6566869 c
11 12 Creole T1 0.7157719 0.0086019 2080 0.6989026 0.7326412 a
13 9 Creole T1 0.7139862 0.0086019 2080 0.6971169 0.7308555 a
12 3 Creole T1 0.6505814 0.0086019 2080 0.6337122 0.6674507 b
14 6 Creole T1 0.6481029 0.0086019 2080 0.6312336 0.6649721 b
15 0 Creole T1 0.5258910 0.0086019 2080 0.5090217 0.5427602 c
21 12 Creole T2 0.6749719 0.0086019 2080 0.6581026 0.6918412 a
23 9 Creole T2 0.6731862 0.0086019 2080 0.6563169 0.6900555 a
22 3 Creole T2 0.6097814 0.0086019 2080 0.5929122 0.6266507 b
24 6 Creole T2 0.6073029 0.0086019 2080 0.5904336 0.6241721 b
25 0 Creole T2 0.4850910 0.0086019 2080 0.4682217 0.5019602 c
31 12 Creole T3 0.6591052 0.0086019 2080 0.6422360 0.6759745 a
33 9 Creole T3 0.6573195 0.0086019 2080 0.6404503 0.6741888 a
32 3 Creole T3 0.5939148 0.0086019 2080 0.5770455 0.6107840 b
34 6 Creole T3 0.5914362 0.0086019 2080 0.5745669 0.6083055 b
35 0 Creole T3 0.4692243 0.0086019 2080 0.4523550 0.4860936 c
41 12 Creole T4 0.6322386 0.0086019 2080 0.6153693 0.6491078 a
43 9 Creole T4 0.6304529 0.0086019 2080 0.6135836 0.6473221 a
42 3 Creole T4 0.5670481 0.0086019 2080 0.5501788 0.5839174 b
44 6 Creole T4 0.5645695 0.0086019 2080 0.5477003 0.5814388 b
45 0 Creole T4 0.4423576 0.0086019 2080 0.4254883 0.4592269 c
51 12 Creole T5 0.8092386 0.0086019 2080 0.7923693 0.8261078 a
53 9 Creole T5 0.8074529 0.0086019 2080 0.7905836 0.8243221 a
52 3 Creole T5 0.7440481 0.0086019 2080 0.7271788 0.7609174 b
54 6 Creole T5 0.7415695 0.0086019 2080 0.7247003 0.7584388 b
55 0 Creole T5 0.6193576 0.0086019 2080 0.6024883 0.6362269 c
61 12 Creole T6 0.7740386 0.0086019 2080 0.7571693 0.7909078 a
63 9 Creole T6 0.7722529 0.0086019 2080 0.7553836 0.7891221 a
62 3 Creole T6 0.7088481 0.0086019 2080 0.6919788 0.7257174 b
64 6 Creole T6 0.7063695 0.0086019 2080 0.6895003 0.7232388 b
65 0 Creole T6 0.5841576 0.0086019 2080 0.5672883 0.6010269 c
6 12 Hybrid T0 0.7764386 0.0086019 2080 0.7595693 0.7933078 a
8 9 Hybrid T0 0.7746529 0.0086019 2080 0.7577836 0.7915221 a
7 3 Hybrid T0 0.7112481 0.0086019 2080 0.6943788 0.7281174 b
9 6 Hybrid T0 0.7087695 0.0086019 2080 0.6919003 0.7256388 b
10 0 Hybrid T0 0.5865576 0.0086019 2080 0.5696883 0.6034269 c
16 12 Hybrid T1 0.7279719 0.0086019 2080 0.7111026 0.7448412 a
18 9 Hybrid T1 0.7261862 0.0086019 2080 0.7093169 0.7430555 a
17 3 Hybrid T1 0.6627814 0.0086019 2080 0.6459122 0.6796507 b
19 6 Hybrid T1 0.6603029 0.0086019 2080 0.6434336 0.6771721 b
20 0 Hybrid T1 0.5380910 0.0086019 2080 0.5212217 0.5549602 c
26 12 Hybrid T2 0.7881052 0.0086019 2080 0.7712360 0.8049745 a
28 9 Hybrid T2 0.7863195 0.0086019 2080 0.7694503 0.8031888 a
27 3 Hybrid T2 0.7229148 0.0086019 2080 0.7060455 0.7397840 b
29 6 Hybrid T2 0.7204362 0.0086019 2080 0.7035669 0.7373055 b
30 0 Hybrid T2 0.5982243 0.0086019 2080 0.5813550 0.6150936 c
36 12 Hybrid T3 0.7332386 0.0086019 2080 0.7163693 0.7501078 a
38 9 Hybrid T3 0.7314529 0.0086019 2080 0.7145836 0.7483221 a
37 3 Hybrid T3 0.6680481 0.0086019 2080 0.6511788 0.6849174 b
39 6 Hybrid T3 0.6655695 0.0086019 2080 0.6487003 0.6824388 b
40 0 Hybrid T3 0.5433576 0.0086019 2080 0.5264883 0.5602269 c
46 12 Hybrid T4 0.7735052 0.0086019 2080 0.7566360 0.7903745 a
48 9 Hybrid T4 0.7717195 0.0086019 2080 0.7548503 0.7885888 a
47 3 Hybrid T4 0.7083148 0.0086019 2080 0.6914455 0.7251840 b
49 6 Hybrid T4 0.7058362 0.0086019 2080 0.6889669 0.7227055 b
50 0 Hybrid T4 0.5836243 0.0086019 2080 0.5667550 0.6004936 c
56 12 Hybrid T5 0.7615719 0.0086019 2080 0.7447026 0.7784412 a
58 9 Hybrid T5 0.7597862 0.0086019 2080 0.7429169 0.7766555 a
57 3 Hybrid T5 0.6963814 0.0086019 2080 0.6795122 0.7132507 b
59 6 Hybrid T5 0.6939029 0.0086019 2080 0.6770336 0.7107721 b
60 0 Hybrid T5 0.5716910 0.0086019 2080 0.5548217 0.5885602 c
66 12 Hybrid T6 0.7741052 0.0086019 2080 0.7572360 0.7909745 a
68 9 Hybrid T6 0.7723195 0.0086019 2080 0.7554503 0.7891888 a
67 3 Hybrid T6 0.7089148 0.0086019 2080 0.6920455 0.7257840 b
69 6 Hybrid T6 0.7064362 0.0086019 2080 0.6895669 0.7233055 b
70 0 Hybrid T6 0.5842243 0.0086019 2080 0.5673550 0.6010936 c

p1a <- mc %>% 
  plot_smr(type = "line"
           , x = "tiempo"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Seed weight (g)"
           , xlab = "Time (h)"
           , glab = "Variety"
           , ylimits = c(0, 1, 0.2)
           ) + 
  facet_wrap(. ~ trat, ncol = 2)

p1a

6.1.2 Porcentaje de Germination

trait <- "pg"
fb <- germination

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers %>% kable()
index bloque trat variedad pg resi res_MAD rawp.BHStud adjp bholm out_flag

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: pg
##               Df  Sum Sq Mean Sq F value   Pr(>F)   
## bloque         2   633.3   316.7  0.6222 0.544582   
## trat           6  7000.0  1166.7  2.2922 0.065673 . 
## variedad       1  4609.5  4609.5  9.0565 0.005753 **
## trat:variedad  6  6857.1  1142.9  2.2454 0.070466 . 
## Residuals     26 13233.3   509.0                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
variedad trat emmean SE df lower.CL upper.CL group
2 Creole T0 86.66667 13.02529 26 59.8928044 113.44053 a
1 Hybrid T0 63.33333 13.02529 26 36.5594710 90.10720 a
3 Hybrid T1 70.00000 13.02529 26 43.2261377 96.77386 a
4 Creole T1 30.00000 13.02529 26 3.2261377 56.77386 b
5 Hybrid T2 56.66667 13.02529 26 29.8928044 83.44053 a
6 Creole T2 36.66667 13.02529 26 9.8928044 63.44053 a
7 Hybrid T3 66.66667 13.02529 26 39.8928044 93.44053 a
8 Creole T3 16.66667 13.02529 26 -10.1071956 43.44053 b
9 Hybrid T4 76.66667 13.02529 26 49.8928044 103.44053 a
10 Creole T4 26.66667 13.02529 26 -0.1071956 53.44053 b
11 Hybrid T5 70.00000 13.02529 26 43.2261377 96.77386 a
12 Creole T5 70.00000 13.02529 26 43.2261377 96.77386 a
13 Hybrid T6 43.33333 13.02529 26 16.5594710 70.10720 a
14 Creole T6 33.33333 13.02529 26 6.5594710 60.10720 a

p1b <- mc %>% 
  plot_smr(type = "bar"
           , x = "trat"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Germination ('%')"
           , xlab = "Treatments"
           , glab = "Variety"
           , ylimits = c(0, 120, 20)
           ) 

p1b

6.1.3 Velocidad de germinación

trait <- "vg"
fb <- germination

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers %>% kable()
index bloque trat variedad vg resi res_MAD rawp.BHStud adjp bholm out_flag
7 7 1 T2 Creole 1 -1.666667 -3.372454 0.0007450 0.0007450159 0.0305456 OUTLIER
34 34 1 T4 Hybrid 5 1.888889 3.822114 0.0001323 0.0001323123 0.0055571 OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: vg
##               Df  Sum Sq Mean Sq F value   Pr(>F)   
## bloque         2  1.3321 0.66607  1.3664 0.274150   
## trat           6  8.6594 1.44323  2.9608 0.026214 * 
## variedad       1  2.0003 2.00025  4.1035 0.054051 . 
## trat:variedad  6 11.5622 1.92703  3.9533 0.006872 **
## Residuals     24 11.6989 0.48745                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
variedad trat emmean SE df lower.CL upper.CL group
2 Creole T0 2.888889 0.4030931 24 2.0569455 3.720832 a
1 Hybrid T0 2.333333 0.4030931 24 1.5013900 3.165277 a
3 Hybrid T1 3.277778 0.4030931 24 2.4458344 4.109721 a
4 Creole T1 1.500000 0.4030931 24 0.6680567 2.331943 b
6 Creole T2 3.379630 0.5004960 24 2.3466566 4.412603 a
5 Hybrid T2 2.833333 0.4030931 24 2.0013900 3.665277 a
7 Hybrid T3 3.833333 0.4030931 24 3.0013900 4.665277 a
8 Creole T3 1.666667 0.4030931 24 0.8347233 2.498610 b
9 Hybrid T4 2.046296 0.5004960 24 1.0133232 3.079269 a
10 Creole T4 1.333333 0.4030931 24 0.5013900 2.165277 a
12 Creole T5 3.055556 0.4030931 24 2.2236122 3.887499 a
11 Hybrid T5 3.000000 0.4030931 24 2.1680567 3.831943 a
14 Creole T6 2.333333 0.4030931 24 1.5013900 3.165277 a
13 Hybrid T6 1.833333 0.4030931 24 1.0013900 2.665277 a

p1c <- mc %>% 
  plot_smr(type = "bar"
           , x = "trat"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Germination speed (days)"
           , xlab = "Treatments"
           , glab = "Variety"
           , ylimits = c(0, 6, 1)
           ) 

p1c

6.1.4 Indice de germinación

trait <- "ig"
fb <- germination

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers %>% kable()
index bloque trat variedad ig resi res_MAD rawp.BHStud adjp bholm out_flag
25 25 1 T1 Hybrid 0.2 -1.466667 -3.29751 0.0009755 0.0009754607 0.0409693 OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: ig
##               Df  Sum Sq Mean Sq F value   Pr(>F)   
## bloque         2  0.3050  0.1525  0.4149 0.664896   
## trat           6 10.3540  1.7257  4.6949 0.002507 **
## variedad       1  3.8850  3.8850 10.5697 0.003278 **
## trat:variedad  6  6.5489  1.0915  2.9695 0.024965 * 
## Residuals     25  9.1890  0.3676                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
variedad trat emmean SE df lower.CL upper.CL group
2 Creole T0 2.6000000 0.3500293 25 1.8791012 3.3208988 a
1 Hybrid T0 1.7666667 0.3500293 25 1.0457678 2.4875655 a
3 Hybrid T1 2.3679487 0.4341579 25 1.4737838 3.2621137 a
4 Creole T1 0.6000000 0.3500293 25 -0.1208988 1.3208988 b
5 Hybrid T2 1.1333333 0.3500293 25 0.4124345 1.8542322 a
6 Creole T2 0.5666667 0.3500293 25 -0.1542322 1.2875655 a
7 Hybrid T3 1.2333333 0.3500293 25 0.5124345 1.9542322 a
8 Creole T3 0.1666667 0.3500293 25 -0.5542322 0.8875655 b
9 Hybrid T4 1.9666667 0.3500293 25 1.2457678 2.6875655 a
10 Creole T4 0.5333333 0.3500293 25 -0.1875655 1.2542322 b
11 Hybrid T5 1.7000000 0.3500293 25 0.9791012 2.4208988 a
12 Creole T5 1.6666667 0.3500293 25 0.9457678 2.3875655 a
13 Hybrid T6 1.0666667 0.3500293 25 0.3457678 1.7875655 a
14 Creole T6 0.5333333 0.3500293 25 -0.1875655 1.2542322 a

p1d <- mc %>% 
  plot_smr(type = "bar"
           , x = "trat"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Germination Index"
           , xlab = "Treatments"
           , glab = "Variety"
           , ylimits = c(0, 5, 1)
           ) 

p1d

6.2 Figura 1

legend <- cowplot::get_plot_component(p1b, 'guide-box-top', return_all = TRUE)

p1i <- list(p1b + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1c + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1d + labs(x = NULL) + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 1
            , labels = c("b", "c", "d")
            ) 

p1il <- list(legend, p1i) %>% 
  plot_grid(plotlist = ., ncol = 1, align = 'v', rel_heights = c(0.05, 1))


plot <- list(p1a, p1il) %>% 
  plot_grid(plotlist = .
            , ncol = 2
            , rel_widths = c(1.5, 1)
            , labels = c("a")
            , label_y = 0.96
            )  
  
plot %>% 
  ggsave2(plot = ., "files/Fig-1.jpg"
         , units = "cm"
         , width = 30
         , height = 25
         )

plot %>% 
  ggsave2(plot = ., "files/Fig-1.eps"
         , units = "cm"
         , width = 30
         , height = 25
         )

knitr::include_graphics("files/Fig-1.jpg")

6.3 Objetivo Específico 2

Identificar el mejor tratamiento que influye positivamente en el crecimiento y desarrollo de plantulas en el cultivo de Maíz morado.

fb <- plantula

rsl <- 4:length(fb) %>% map(\(x) {
  
trait <- names(fb)[x]

cat("\n### ", trait)

lmm <- paste({{trait}}, "~ 1 + (1|trat) + trat*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

cat("\n#### ",  "Diagnostico")

rmout$diagplot %>% print()

cat("\n#### ", "Outliers")

rmout$outliers  %>% kable() %>% print()

cat("\n#### ", "ANOVA")

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model) %>% anova_table %>% kable() %>% print()

cat("\n#### ", "Mean comparison")

mc <- emmeans(model, ~ variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group") %>% 
  rename({{trait}} := "emmean")

mc %>% kable() %>% print()

plot <- mc %>% 
  plot_smr(x = "trat"
           , y = trait
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Treatments"
           , glab = "Variety"
           )

plot

list(mc = mc, plot = plot)
  
})

6.3.1 raiz_lgtd

6.3.1.1 Diagnostico

6.3.1.2 Outliers

index trat variedad raiz_lgtd resi res_MAD rawp.BHStud adjp bholm out_flag

6.3.1.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
trat 6 113.028571428572 18.8380952380953 2.20354897748071 0.0442610263913077 *
variedad 1 2.30476190476198 2.30476190476198 0.269594970955686 0.604189403305695 ns
trat:variedad 6 240.761904761907 40.1269841269844 4.69377470093635 0.000173605924663632 ***
Residuals 196 1675.6 8.54897959183673
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.1.4 Mean comparison

variedad trat raiz_lgtd SE df lower.CL upper.CL group
1 Hybrid T0 16.00000 0.7549384 196 14.511155 17.48885 a
2 Creole T0 11.00000 0.7549384 196 9.511155 12.48885 b
4 Creole T1 14.00000 0.7549384 196 12.511155 15.48885 a
3 Hybrid T1 13.93333 0.7549384 196 12.444488 15.42218 a
5 Hybrid T2 13.60000 0.7549384 196 12.111155 15.08885 a
6 Creole T2 13.33333 0.7549384 196 11.844488 14.82218 a
8 Creole T3 12.93333 0.7549384 196 11.444488 14.42218 a
7 Hybrid T3 12.46667 0.7549384 196 10.977821 13.95551 a
10 Creole T4 15.13333 0.7549384 196 13.644488 16.62218 a
9 Hybrid T4 14.00000 0.7549384 196 12.511155 15.48885 a
12 Creole T5 15.80000 0.7549384 196 14.311155 17.28885 a
11 Hybrid T5 13.40000 0.7549384 196 11.911155 14.88885 b
13 Hybrid T6 15.06667 0.7549384 196 13.577822 16.55551 a
14 Creole T6 14.80000 0.7549384 196 13.311155 16.28885 a

6.3.2 gsr_raiz

6.3.2.1 Diagnostico

6.3.2.2 Outliers

index trat variedad gsr_raiz resi res_MAD rawp.BHStud adjp bholm out_flag

6.3.2.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
trat 6 2.85575333333333 0.475958888888889 12.0927440494915 0.0000000000150374615277643 ***
variedad 1 0.327257619047619 0.327257619047619 8.3146732160573 0.00437199311126879 **
trat:variedad 6 0.701705714285716 0.116950952380953 2.97138674474315 0.00845464188145274 **
Residuals 196 7.71437333333333 0.0393590476190476
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.2.4 Mean comparison

variedad trat gsr_raiz SE df lower.CL upper.CL group
2 Creole T0 1.2560000 0.0512244 196 1.1549783 1.3570217 a
1 Hybrid T0 1.0853333 0.0512244 196 0.9843116 1.1863550 b
3 Hybrid T1 0.8926667 0.0512244 196 0.7916450 0.9936884 a
4 Creole T1 0.7486667 0.0512244 196 0.6476450 0.8496884 b
5 Hybrid T2 0.9500000 0.0512244 196 0.8489783 1.0510217 a
6 Creole T2 0.9260000 0.0512244 196 0.8249783 1.0270217 a
8 Creole T3 0.8600000 0.0512244 196 0.7589783 0.9610217 a
7 Hybrid T3 0.7760000 0.0512244 196 0.6749783 0.8770217 a
10 Creole T4 0.9846667 0.0512244 196 0.8836450 1.0856884 a
9 Hybrid T4 0.7840000 0.0512244 196 0.6829783 0.8850217 b
12 Creole T5 1.1066667 0.0512244 196 1.0056450 1.2076884 a
11 Hybrid T5 0.9280000 0.0512244 196 0.8269783 1.0290217 b
14 Creole T6 1.0513333 0.0512244 196 0.9503116 1.1523550 a
13 Hybrid T6 0.9646667 0.0512244 196 0.8636450 1.0656884 a

6.3.3 num_raiz

6.3.3.1 Diagnostico

6.3.3.2 Outliers

index trat variedad num_raiz resi res_MAD rawp.BHStud adjp bholm out_flag

6.3.3.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
trat 6 457.161904761906 76.193650793651 9.76160594968335 0.00000000208792569898294 ***
variedad 1 4.28571428571413 4.28571428571413 0.549067456858964 0.45958580814979 ns
trat:variedad 6 153.714285714286 25.6190476190477 3.2822032421126 0.00424059446258091 **
Residuals 196 1529.86666666667 7.80544217687077
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.3.4 Mean comparison

variedad trat num_raiz SE df lower.CL upper.CL group
1 Hybrid T0 11.33333 0.7213618 196 9.910706 12.75596 a
2 Creole T0 10.86667 0.7213618 196 9.444039 12.28929 a
3 Hybrid T1 15.73333 0.7213618 196 14.310706 17.15596 a
4 Creole T1 14.53333 0.7213618 196 13.110706 15.95596 a
6 Creole T2 15.06667 0.7213618 196 13.644039 16.48929 a
5 Hybrid T2 12.00000 0.7213618 196 10.577373 13.42263 b
7 Hybrid T3 12.53333 0.7213618 196 11.110706 13.95596 a
8 Creole T3 10.66667 0.7213618 196 9.244039 12.08929 a
10 Creole T4 13.40000 0.7213618 196 11.977373 14.82263 a
9 Hybrid T4 10.93333 0.7213618 196 9.510706 12.35596 b
11 Hybrid T5 11.80000 0.7213618 196 10.377373 13.22263 a
12 Creole T5 11.33333 0.7213618 196 9.910706 12.75596 a
14 Creole T6 10.73333 0.7213618 196 9.310706 12.15596 a
13 Hybrid T6 10.26667 0.7213618 196 8.844039 11.68929 a

6.3.4 peso_fres_raiz

6.3.4.1 Diagnostico

6.3.4.2 Outliers

index trat variedad peso_fres_raiz resi res_MAD rawp.BHStud adjp bholm out_flag

6.3.4.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
trat 6 73.5104057142858 12.2517342857143 6.56141211693855 0.00000248176481747279 ***
variedad 1 0.00092190476190473 0.00092190476190473 0.000493725780723001 0.982295115363446 ns
trat:variedad 6 36.2561847619047 6.04269746031745 3.23616456335913 0.00469927715193408 **
Residuals 196 365.979133333333 1.86724047619048
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.4.4 Mean comparison

variedad trat peso_fres_raiz SE df lower.CL upper.CL group
2 Creole T0 4.698000 0.3528211 196 4.002187 5.393813 a
1 Hybrid T0 4.537333 0.3528211 196 3.841520 5.233146 a
4 Creole T1 6.624000 0.3528211 196 5.928187 7.319813 a
3 Hybrid T1 6.082667 0.3528211 196 5.386854 6.778480 a
6 Creole T2 4.990000 0.3528211 196 4.294187 5.685813 a
5 Hybrid T2 4.738667 0.3528211 196 4.042854 5.434480 a
7 Hybrid T3 5.388000 0.3528211 196 4.692187 6.083813 a
8 Creole T3 4.208000 0.3528211 196 3.512187 4.903813 b
10 Creole T4 5.346000 0.3528211 196 4.650187 6.041813 a
9 Hybrid T4 3.858667 0.3528211 196 3.162854 4.554480 b
11 Hybrid T5 5.245333 0.3528211 196 4.549520 5.941146 a
12 Creole T5 4.418000 0.3528211 196 3.722187 5.113813 a
13 Hybrid T6 5.728667 0.3528211 196 5.032854 6.424480 a
14 Creole T6 5.324667 0.3528211 196 4.628854 6.020480 a

6.3.5 peso_seco_raiz

6.3.5.1 Diagnostico

6.3.5.2 Outliers

index trat variedad peso_seco_raiz resi res_MAD rawp.BHStud adjp bholm out_flag
124 124 T1 Hybrid 2.60 1.318667 4.904936 0.0000009 0.0000009345758 0.0001953 OUTLIER
153 153 T3 Hybrid 2.88 1.516000 5.638941 0.0000000 0.0000000171099 0.0000036 OUTLIER
193 193 T5 Hybrid 1.94 1.170000 4.351953 0.0000135 0.0000134930040 0.0028065 OUTLIER

6.3.5.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
trat 6 8.89570540896218 1.48261756816036 14.9702912365665 0.0000000000000489299838764944 ***
variedad 1 0.208061338505747 0.208061338505747 2.10083766669894 0.148841410422638 ns
trat:variedad 6 0.273239581034483 0.0455399301724139 0.459825940427679 0.837389504298845 ns
Residuals 193 19.1142033333333 0.099037322970639
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.5.4 Mean comparison

variedad trat peso_seco_raiz SE df lower.CL upper.CL group
1 Hybrid T0 0.6646667 0.0812557 193 0.5044035 0.8249299 a
2 Creole T0 0.6346667 0.0812557 193 0.4744035 0.7949299 a
4 Creole T1 1.2140000 0.0812557 193 1.0537368 1.3742632 a
3 Hybrid T1 1.1871429 0.0841076 193 1.0212547 1.3530310 a
5 Hybrid T2 0.8340000 0.0812557 193 0.6737368 0.9942632 a
6 Creole T2 0.8133333 0.0812557 193 0.6530701 0.9735965 a
7 Hybrid T3 1.2557143 0.0841076 193 1.0898261 1.4216024 a
8 Creole T3 1.0566667 0.0812557 193 0.8964035 1.2169299 a
9 Hybrid T4 0.9240000 0.0812557 193 0.7637368 1.0842632 a
10 Creole T4 0.8433333 0.0812557 193 0.6830701 1.0035965 a
11 Hybrid T5 0.6864286 0.0841076 193 0.5205404 0.8523167 a
12 Creole T5 0.5526667 0.0812557 193 0.3924035 0.7129299 a
13 Hybrid T6 0.9426667 0.0812557 193 0.7824035 1.1029299 a
14 Creole T6 0.9320000 0.0812557 193 0.7717368 1.0922632 a

6.3.6 alt_planta

6.3.6.1 Diagnostico

6.3.6.2 Outliers

index trat variedad alt_planta resi res_MAD rawp.BHStud adjp bholm out_flag

6.3.6.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
trat 6 8203.78095238095 1367.29682539682 50.9551611949127 0.000000000000000000000000000000000000184040121052396 ***
variedad 1 20.7428571428572 20.7428571428572 0.773025732031944 0.380359284820424 ns
trat:variedad 6 1204.52380952381 200.753968253969 7.48151434486839 0.000000313700914208267 ***
Residuals 196 5259.33333333333 26.8333333333333
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.6.4 Mean comparison

variedad trat alt_planta SE df lower.CL upper.CL group
2 Creole T0 30.80000 1.337493 196 28.16227 33.43773 a
1 Hybrid T0 26.13333 1.337493 196 23.49561 28.77106 b
3 Hybrid T1 46.33333 1.337493 196 43.69561 48.97106 a
4 Creole T1 40.26667 1.337493 196 37.62894 42.90439 b
5 Hybrid T2 44.20000 1.337493 196 41.56227 46.83773 a
6 Creole T2 39.40000 1.337493 196 36.76227 42.03773 b
8 Creole T3 38.26667 1.337493 196 35.62894 40.90439 a
7 Hybrid T3 37.06667 1.337493 196 34.42894 39.70439 a
10 Creole T4 40.53333 1.337493 196 37.89561 43.17106 a
9 Hybrid T4 33.06667 1.337493 196 30.42894 35.70439 b
11 Hybrid T5 30.26667 1.337493 196 27.62894 32.90439 a
12 Creole T5 27.53333 1.337493 196 24.89561 30.17106 a
13 Hybrid T6 28.80000 1.337493 196 26.16227 31.43773 a
14 Creole T6 24.66667 1.337493 196 22.02894 27.30439 b

6.3.7 gsr_tallo

6.3.7.1 Diagnostico

6.3.7.2 Outliers

index trat variedad gsr_tallo resi res_MAD rawp.BHStud adjp bholm out_flag
3 3 T0 Creole 6.59 2.118667 4.170295 0.0000304 0.00003042056 0.0063579 OUTLIER
139 139 T2 Hybrid 3.04 -2.168000 -4.267401 0.0000198 0.00001977638 0.0041530 OUTLIER

6.3.7.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
trat 6 36.8710544473915 6.14517574123192 16.4230125647216 0.00000000000000285227440100774 ***
variedad 1 1.40401584033855 1.40401584033855 3.7522392780789 0.0541891483916545 ns
trat:variedad 6 5.63568810054818 0.939281350091364 2.51023405414711 0.0231302956641592 *
Residuals 194 72.5910723809524 0.374180785468827
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.7.4 Mean comparison

variedad trat gsr_tallo SE df lower.CL upper.CL group
2 Creole T0 4.320000 0.1634846 194 3.997565 4.642435 a
1 Hybrid T0 3.890667 0.1579411 194 3.579165 4.202169 a
3 Hybrid T1 4.917333 0.1579411 194 4.605831 5.228835 a
4 Creole T1 4.849333 0.1579411 194 4.537831 5.160835 a
5 Hybrid T2 5.362857 0.1634846 194 5.040422 5.685292 a
6 Creole T2 5.032000 0.1579411 194 4.720498 5.343502 a
8 Creole T3 4.509333 0.1579411 194 4.197831 4.820835 a
7 Hybrid T3 4.016000 0.1579411 194 3.704498 4.327502 b
10 Creole T4 4.316667 0.1579411 194 4.005165 4.628169 a
9 Hybrid T4 3.704000 0.1579411 194 3.392498 4.015502 b
12 Creole T5 4.195333 0.1579411 194 3.883831 4.506835 a
11 Hybrid T5 4.066667 0.1579411 194 3.755165 4.378169 a
13 Hybrid T6 4.218000 0.1579411 194 3.906498 4.529502 a
14 Creole T6 4.095333 0.1579411 194 3.783831 4.406835 a

6.3.8 nhp_hoja

6.3.8.1 Diagnostico

6.3.8.2 Outliers

index trat variedad nhp_hoja resi res_MAD rawp.BHStud adjp bholm out_flag
4 4 T0 Creole 6 1.0000000 10.117361 0.0000000 0.0000000000000000000 0.0000000 OUTLIER
5 5 T0 Creole 4 -1.0000000 -10.117361 0.0000000 0.0000000000000000000 0.0000000 OUTLIER
16 16 T1 Creole 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
17 17 T1 Creole 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
18 18 T1 Creole 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
19 19 T1 Creole 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
20 20 T1 Creole 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
21 21 T1 Creole 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
22 22 T1 Creole 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
23 23 T1 Creole 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
24 24 T1 Creole 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
25 25 T1 Creole 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
26 26 T1 Creole 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
27 27 T1 Creole 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
28 28 T1 Creole 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
29 29 T1 Creole 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
30 30 T1 Creole 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
31 31 T2 Creole 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
32 32 T2 Creole 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
33 33 T2 Creole 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
34 34 T2 Creole 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
35 35 T2 Creole 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
36 36 T2 Creole 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
37 37 T2 Creole 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
38 38 T2 Creole 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
39 39 T2 Creole 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
40 40 T2 Creole 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
41 41 T2 Creole 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
42 42 T2 Creole 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
43 43 T2 Creole 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
44 44 T2 Creole 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
45 45 T2 Creole 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
51 51 T3 Creole 6 0.8666667 8.768380 0.0000000 0.0000000000000000000 0.0000000 OUTLIER
57 57 T3 Creole 6 0.8666667 8.768380 0.0000000 0.0000000000000000000 0.0000000 OUTLIER
62 62 T4 Creole 6 0.7333333 7.419398 0.0000000 0.0000000000001176836 0.0000000 OUTLIER
65 65 T4 Creole 6 0.7333333 7.419398 0.0000000 0.0000000000001176836 0.0000000 OUTLIER
66 66 T4 Creole 6 0.7333333 7.419398 0.0000000 0.0000000000001176836 0.0000000 OUTLIER
72 72 T4 Creole 6 0.7333333 7.419398 0.0000000 0.0000000000001176836 0.0000000 OUTLIER
88 88 T5 Creole 6 0.9333333 9.442871 0.0000000 0.0000000000000000000 0.0000000 OUTLIER
112 112 T0 Hybrid 4 -0.6666667 -6.744908 0.0000000 0.0000000000153124180 0.0000000 OUTLIER
114 114 T0 Hybrid 4 -0.6666667 -6.744908 0.0000000 0.0000000000153124180 0.0000000 OUTLIER
116 116 T0 Hybrid 4 -0.6666667 -6.744908 0.0000000 0.0000000000153124180 0.0000000 OUTLIER
119 119 T0 Hybrid 4 -0.6666667 -6.744908 0.0000000 0.0000000000153124180 0.0000000 OUTLIER
120 120 T0 Hybrid 4 -0.6666667 -6.744908 0.0000000 0.0000000000153124180 0.0000000 OUTLIER
121 121 T1 Hybrid 6 0.5333333 5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
122 122 T1 Hybrid 6 0.5333333 5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
123 123 T1 Hybrid 5 -0.4666667 -4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
124 124 T1 Hybrid 6 0.5333333 5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
125 125 T1 Hybrid 6 0.5333333 5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
126 126 T1 Hybrid 6 0.5333333 5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
127 127 T1 Hybrid 6 0.5333333 5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
128 128 T1 Hybrid 5 -0.4666667 -4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
129 129 T1 Hybrid 5 -0.4666667 -4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
130 130 T1 Hybrid 5 -0.4666667 -4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
131 131 T1 Hybrid 5 -0.4666667 -4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
132 132 T1 Hybrid 6 0.5333333 5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
133 133 T1 Hybrid 5 -0.4666667 -4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
134 134 T1 Hybrid 5 -0.4666667 -4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
135 135 T1 Hybrid 5 -0.4666667 -4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
136 136 T2 Hybrid 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
137 137 T2 Hybrid 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
138 138 T2 Hybrid 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
139 139 T2 Hybrid 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
140 140 T2 Hybrid 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
141 141 T2 Hybrid 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
142 142 T2 Hybrid 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
143 143 T2 Hybrid 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
144 144 T2 Hybrid 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
145 145 T2 Hybrid 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
146 146 T2 Hybrid 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
147 147 T2 Hybrid 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
148 148 T2 Hybrid 6 0.4666667 4.721435 0.0000023 0.0000023418611010406 0.0003817 OUTLIER
149 149 T2 Hybrid 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
150 150 T2 Hybrid 5 -0.5333333 -5.395926 0.0000001 0.0000000681710119466 0.0000130 OUTLIER
152 152 T3 Hybrid 6 0.8666667 8.768380 0.0000000 0.0000000000000000000 0.0000000 OUTLIER
155 155 T3 Hybrid 6 0.8666667 8.768380 0.0000000 0.0000000000000000000 0.0000000 OUTLIER
166 166 T4 Hybrid 6 0.8666667 8.768380 0.0000000 0.0000000000000000000 0.0000000 OUTLIER
173 173 T4 Hybrid 6 0.8666667 8.768380 0.0000000 0.0000000000000000000 0.0000000 OUTLIER
181 181 T5 Hybrid 6 0.9333333 9.442871 0.0000000 0.0000000000000000000 0.0000000 OUTLIER

6.3.8.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
trat 4 0.000000000000000000000000000448210187366446 0.000000000000000000000000000112052546841611 1.17465980750084 0.325411307782507 ns
variedad 1 0.0000000000000000000000000000724842873482364 0.0000000000000000000000000000724842873482364 0.759861167133205 0.385098313680927 ns
trat:variedad 4 0.000000000000000000000000000345717525650355 0.0000000000000000000000000000864293814125887 0.906049200970291 0.46279233587861 ns
Residuals 121 0.0000000000000000000000000115423700387614 0.0000000000000000000000000000953914879236483
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.8.4 Mean comparison

variedad trat nhp_hoja SE df lower.CL upper.CL group
2 Creole T0 5 0 121 5 5 a
1 Hybrid T0 5 0 121 5 5 b
3 Hybrid T3 5 0 121 5 5 a
4 Creole T3 5 0 121 5 5 a
5 Hybrid T4 5 0 121 5 5 a
6 Creole T4 5 0 121 5 5 a
7 Hybrid T5 5 0 121 5 5 a
8 Creole T5 5 0 121 5 5 a
9 Hybrid T6 5 0 121 5 5 a
10 Creole T6 5 0 121 5 5 a

6.3.9 larg_hoja

6.3.9.1 Diagnostico

6.3.9.2 Outliers

index trat variedad larg_hoja resi res_MAD rawp.BHStud adjp bholm out_flag

6.3.9.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
trat 6 3517.25714285714 586.209523809524 44.1413789570741 0.000000000000000000000000000000000679814077816785 ***
variedad 1 12.8761904761905 12.8761904761905 0.96957278967319 0.326000686912924 ns
trat:variedad 6 830.857142857144 138.476190476191 10.4272103268108 0.000000000500170136061446 ***
Residuals 196 2602.93333333333 13.2802721088435
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.9.4 Mean comparison

variedad trat larg_hoja SE df lower.CL upper.CL group
2 Creole T0 26.20000 0.9409312 196 24.34435 28.05565 a
1 Hybrid T0 22.66667 0.9409312 196 20.81102 24.52232 b
3 Hybrid T1 36.26667 0.9409312 196 34.41102 38.12232 a
4 Creole T1 28.33333 0.9409312 196 26.47768 30.18898 b
6 Creole T2 34.80000 0.9409312 196 32.94435 36.65565 a
5 Hybrid T2 33.86667 0.9409312 196 32.01102 35.72232 a
7 Hybrid T3 28.93333 0.9409312 196 27.07768 30.78898 a
8 Creole T3 28.60000 0.9409312 196 26.74435 30.45565 a
10 Creole T4 28.26667 0.9409312 196 26.41102 30.12232 a
9 Hybrid T4 23.33333 0.9409312 196 21.47768 25.18898 b
11 Hybrid T5 26.46667 0.9409312 196 24.61102 28.32232 a
12 Creole T5 23.40000 0.9409312 196 21.54435 25.25565 b
13 Hybrid T6 23.06667 0.9409312 196 21.21102 24.92232 a
14 Creole T6 21.53333 0.9409312 196 19.67768 23.38898 a

6.3.10 grs_hoja

6.3.10.1 Diagnostico

6.3.10.2 Outliers

index trat variedad grs_hoja resi res_MAD rawp.BHStud adjp bholm out_flag
193 193 T5 Hybrid 1.43 0.5793333 3.757259 0.0001718 0.0001717844 0.0359029 OUTLIER
197 197 T6 Hybrid 1.45 0.6780000 4.397161 0.0000110 0.0000109676 0.0023032 OUTLIER

6.3.10.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
trat 6 1.87838186229001 0.313063643715002 10.961029990429 0.000000000166359834805947 ***
variedad 1 0.000634076170869784 0.000634076170869784 0.022200367448119 0.881710078225411 ns
trat:variedad 6 0.632610833517145 0.105435138919524 3.69151047380647 0.00169800034430951 **
Residuals 194 5.54093428571428 0.0285615169366716
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.10.4 Mean comparison

variedad trat grs_hoja SE df lower.CL upper.CL group
2 Creole T0 0.9700000 0.0436360 194 0.8839381 1.0560619 a
1 Hybrid T0 0.8846667 0.0436360 194 0.7986048 0.9707285 a
3 Hybrid T1 0.8446667 0.0436360 194 0.7586048 0.9307285 a
4 Creole T1 0.6346667 0.0436360 194 0.5486048 0.7207285 b
5 Hybrid T2 0.7553333 0.0436360 194 0.6692715 0.8413952 a
6 Creole T2 0.7006667 0.0436360 194 0.6146048 0.7867285 a
7 Hybrid T3 0.6880000 0.0436360 194 0.6019381 0.7740619 a
8 Creole T3 0.6300000 0.0436360 194 0.5439381 0.7160619 a
10 Creole T4 0.6853333 0.0436360 194 0.5992715 0.7713952 a
9 Hybrid T4 0.6366667 0.0436360 194 0.5506048 0.7227285 a
12 Creole T5 0.9406667 0.0436360 194 0.8546048 1.0267285 a
11 Hybrid T5 0.8092857 0.0451676 194 0.7202032 0.8983682 b
14 Creole T6 0.8126667 0.0436360 194 0.7266048 0.8987285 a
13 Hybrid T6 0.7235714 0.0451676 194 0.6344889 0.8126539 a

6.3.11 anch_hoja

6.3.11.1 Diagnostico

6.3.11.2 Outliers

index trat variedad anch_hoja resi res_MAD rawp.BHStud adjp bholm out_flag
141 141 T2 Hybrid 30.38 8.610667 3.680491 0.0002328 0.0002327851 0.0488849 OUTLIER

6.3.11.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
trat 6 251.760531761535 41.9600886269226 6.40463281705183 0.00000355932128610583 ***
variedad 1 0.0145229907818354 0.0145229907818354 0.00221673562680236 0.962495880334166 ns
trat:variedad 6 116.659083347477 19.4431805579129 2.96773520609711 0.00853268555104408 **
Residuals 195 1277.54666285714 6.55152134798535
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.11.4 Mean comparison

variedad trat anch_hoja SE df lower.CL upper.CL group
2 Creole T0 18.86800 0.6608843 195 17.56460 20.17140 a
1 Hybrid T0 16.61133 0.6608843 195 15.30793 17.91473 b
3 Hybrid T1 20.04333 0.6608843 195 18.73993 21.34673 a
4 Creole T1 18.95867 0.6608843 195 17.65527 20.26207 a
5 Hybrid T2 21.15429 0.6840803 195 19.80514 22.50343 a
6 Creole T2 19.03467 0.6608843 195 17.73127 20.33807 b
8 Creole T3 19.05067 0.6608843 195 17.74727 20.35407 a
7 Hybrid T3 17.51800 0.6608843 195 16.21460 18.82140 a
10 Creole T4 17.06933 0.6608843 195 15.76593 18.37273 a
9 Hybrid T4 16.07400 0.6608843 195 14.77060 17.37740 a
11 Hybrid T5 19.51800 0.6608843 195 18.21460 20.82140 a
12 Creole T5 18.28667 0.6608843 195 16.98327 19.59007 a
13 Hybrid T6 17.86067 0.6608843 195 16.55727 19.16407 a
14 Creole T6 17.55467 0.6608843 195 16.25127 18.85807 a

6.3.12 peso_fres_brote

6.3.12.1 Diagnostico

6.3.12.2 Outliers

index trat variedad peso_fres_brote resi res_MAD rawp.BHStud adjp bholm out_flag

6.3.12.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
trat 6 225.698459161905 37.6164098603175 28.7634400302011 0.00000000000000000000000146687997518286 ***
variedad 1 0.375666304761905 0.375666304761905 0.287253761550096 0.59259348215828 ns
trat:variedad 6 67.1975631619048 11.1995938603175 8.56378499596482 0.0000000284330749762502 ***
Residuals 196 256.3259584 1.30778550204082
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.12.4 Mean comparison

variedad trat peso_fres_brote SE df lower.CL upper.CL group
2 Creole T0 5.392000 0.2952722 196 4.809681 5.974319 a
1 Hybrid T0 3.613333 0.2952722 196 3.031015 4.195652 b
3 Hybrid T1 7.640000 0.2952722 196 7.057681 8.222319 a
4 Creole T1 5.770000 0.2952722 196 5.187681 6.352319 b
5 Hybrid T2 7.163333 0.2952722 196 6.581015 7.745652 a
6 Creole T2 6.562000 0.2952722 196 5.979681 7.144319 a
8 Creole T3 5.699333 0.2952722 196 5.117015 6.281652 a
7 Hybrid T3 4.771333 0.2952722 196 4.189015 5.353652 b
10 Creole T4 5.014000 0.2952722 196 4.431681 5.596319 a
9 Hybrid T4 4.094000 0.2952722 196 3.511681 4.676319 b
11 Hybrid T5 4.720000 0.2952722 196 4.137681 5.302319 a
12 Creole T5 4.684800 0.2952722 196 4.102481 5.267119 a
13 Hybrid T6 4.303333 0.2952722 196 3.721015 4.885652 a
14 Creole T6 3.775333 0.2952722 196 3.193015 4.357652 a

6.3.13 peso_seco_brote

6.3.13.1 Diagnostico

6.3.13.2 Outliers

index trat variedad peso_seco_brote resi res_MAD rawp.BHStud adjp bholm out_flag
42 42 T2 Creole 2.39 1.208667 3.733899 0.0001885 0.00018853852782 0.0384619 OUTLIER
72 72 T4 Creole 3.09 1.768000 5.461830 0.0000000 0.00000004712499 0.0000099 OUTLIER
127 127 T1 Hybrid 3.27 1.246667 3.851291 0.0001175 0.00011749690142 0.0240869 OUTLIER
134 134 T1 Hybrid 0.73 -1.293333 -3.995457 0.0000646 0.00006456965482 0.0133013 OUTLIER
167 167 T4 Hybrid 0.27 -1.193333 -3.686530 0.0002273 0.00022733296863 0.0461486 OUTLIER
169 169 T4 Hybrid 2.85 1.386667 4.283789 0.0000184 0.00001837374658 0.0038034 OUTLIER
170 170 T4 Hybrid 2.93 1.466667 4.530930 0.0000059 0.00000587245145 0.0012215 OUTLIER
172 172 T4 Hybrid 2.98 1.516667 4.685394 0.0000028 0.00000279422122 0.0005840 OUTLIER

6.3.13.3 ANOVA

Factor Df Sum Sq Mean Sq F value Pr(>F) Sig
trat 6 43.1935007107252 7.19891678512086 42.4711267015912 0.000000000000000000000000000000016240034181697 ***
variedad 1 0.0152306451812492 0.0152306451812492 0.0898555547380109 0.764692030144416 ns
trat:variedad 6 3.37242174716203 0.562070291193672 3.31602090495423 0.00398028439930072 **
Residuals 188 31.8662691741592 0.169501431777442
Significance: 0.001 *** 0.01 ** 0.05 *

6.3.13.4 Mean comparison

variedad trat peso_seco_brote SE df lower.CL upper.CL group
2 Creole T0 0.7100000 0.1063019 188 0.5003022 0.9196978 a
1 Hybrid T0 0.4253333 0.1063019 188 0.2156355 0.6350311 a
3 Hybrid T1 2.0269231 0.1141866 188 1.8016715 2.2521747 a
4 Creole T1 1.5046667 0.1063019 188 1.2949689 1.7143645 b
5 Hybrid T2 1.2113333 0.1063019 188 1.0016355 1.4210311 a
6 Creole T2 1.0950000 0.1100329 188 0.8779421 1.3120579 a
8 Creole T3 1.7186667 0.1063019 188 1.5089689 1.9283645 a
7 Hybrid T3 1.4093333 0.1063019 188 1.1996355 1.6190311 b
10 Creole T4 1.1957143 0.1100329 188 0.9786564 1.4127721 a
9 Hybrid T4 1.1745455 0.1241339 188 0.9296712 1.4194197 a
12 Creole T5 0.6500000 0.1063019 188 0.4403022 0.8596978 a
11 Hybrid T5 0.6373333 0.1063019 188 0.4276355 0.8470311 a
14 Creole T6 0.5486667 0.1063019 188 0.3389689 0.7583645 a
13 Hybrid T6 0.4586667 0.1063019 188 0.2489689 0.6683645 a

6.3.14 Figure 2

fig <- 1:length(rsl) %>% map(\(x) { rsl[[x]]$plot }) %>% 
    plot_grid(plotlist = .
              , ncol = 3
              , labels = "auto"
              ) 

fig %>% 
  ggsave2(plot = .
          , "files/Fig-2.jpg"
          , units = "cm"
          , width = 30
          , height = 40
         )

include_graphics("files/Fig-2.jpg")

6.3.15 PCA

blues <- 1:length(rsl) %>% map(\(x) { 
  
  rsl[[x]]$mc %>% 
    select(1:3)
  
  }) %>% 
  Reduce(function(...) merge(..., all = TRUE), .) 

blues %>% str()
## 'data.frame':    14 obs. of  15 variables:
##  $ variedad       : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 2 2 2 ...
##  $ trat           : Factor w/ 7 levels "T0","T1","T2",..: 1 2 3 4 5 6 7 1 2 3 ...
##  $ raiz_lgtd      : num  11 14 13.3 12.9 15.1 ...
##  $ gsr_raiz       : num  1.256 0.749 0.926 0.86 0.985 ...
##  $ num_raiz       : num  10.9 14.5 15.1 10.7 13.4 ...
##  $ peso_fres_raiz : num  4.7 6.62 4.99 4.21 5.35 ...
##  $ peso_seco_raiz : num  0.635 1.214 0.813 1.057 0.843 ...
##  $ alt_planta     : num  30.8 40.3 39.4 38.3 40.5 ...
##  $ gsr_tallo      : num  4.32 4.85 5.03 4.51 4.32 ...
##  $ nhp_hoja       : num  5 NA NA 5 5 ...
##  $ larg_hoja      : num  26.2 28.3 34.8 28.6 28.3 ...
##  $ grs_hoja       : num  0.97 0.635 0.701 0.63 0.685 ...
##  $ anch_hoja      : num  18.9 19 19 19.1 17.1 ...
##  $ peso_fres_brote: num  5.39 5.77 6.56 5.7 5.01 ...
##  $ peso_seco_brote: num  0.71 1.5 1.09 1.72 1.2 ...
pca <- blues %>% 
  select(!c(nhp_hoja)) %>% 
  unite("treat", c(trat, variedad), remove = F, sep = "-") %>% 
  column_to_rownames("treat") %>% 
  PCA(scale.unit = T, quali.sup = c(1:2), graph = F)

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, quali.sup = c(1:2), graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3  Dim.4  Dim.5  Dim.6  Dim.7  Dim.8
## Variance               6.27   2.46   1.22   0.79   0.55   0.32   0.20   0.09
## % of var.             52.22  20.54  10.20   6.57   4.59   2.65   1.64   0.72
## Cumulative % of var.  52.22  72.76  82.96  89.54  94.12  96.77  98.41  99.13
##                       Dim.9 Dim.10 Dim.11 Dim.12
## Variance               0.06   0.03   0.01   0.00
## % of var.              0.53   0.23   0.11   0.01
## Cumulative % of var.  99.66  99.89  99.99 100.00
## 
## Individuals
##                    Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr
## T0-Creole       |  4.15 | -1.64  3.07  0.16 |  2.99 25.86  0.52 | -1.37 10.97
## T1-Creole       |  3.89 |  3.03 10.44  0.61 | -1.65  7.88  0.18 |  1.40 11.38
## T2-Creole       |  3.15 |  2.46  6.89  0.61 |  1.00  2.88  0.10 |  0.02  0.00
## T3-Creole       |  2.86 |  1.26  1.82  0.20 | -0.85  2.08  0.09 | -2.16 27.31
## T4-Creole       |  1.99 |  0.26  0.08  0.02 | -0.77  1.73  0.15 |  0.82  3.89
## T5-Creole       |  3.56 | -2.85  9.28  0.64 |  1.59  7.31  0.20 |  0.67  2.58
## T6-Creole       |  3.08 | -2.74  8.55  0.79 | -0.43  0.53  0.02 |  0.81  3.80
## T0-Hybrid       |  4.00 | -3.75 16.03  0.88 |  0.17  0.08  0.00 |  0.92  4.95
## T1-Hybrid       |  5.13 |  4.70 25.13  0.84 |  0.81  1.90  0.02 |  1.15  7.69
## T2-Hybrid       |  3.97 |  2.81  8.98  0.50 |  2.13 13.09  0.29 | -0.76  3.40
## T3-Hybrid       |  2.92 |  1.07  1.30  0.13 | -2.18 13.77  0.56 | -0.73  3.10
## T4-Hybrid       |  3.68 | -1.68  3.22  0.21 | -2.59 19.46  0.50 | -1.64 15.76
## T5-Hybrid       |  2.02 | -1.06  1.28  0.27 |  0.66  1.26  0.11 | -0.05  0.01
## T6-Hybrid       |  2.69 | -1.85  3.92  0.47 | -0.86  2.17  0.10 |  0.94  5.16
##                  cos2  
## T0-Creole        0.11 |
## T1-Creole        0.13 |
## T2-Creole        0.00 |
## T3-Creole        0.57 |
## T4-Creole        0.17 |
## T5-Creole        0.03 |
## T6-Creole        0.07 |
## T0-Hybrid        0.05 |
## T1-Hybrid        0.05 |
## T2-Hybrid        0.04 |
## T3-Hybrid        0.06 |
## T4-Hybrid        0.20 |
## T5-Hybrid        0.00 |
## T6-Hybrid        0.12 |
## 
## Variables
##                   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr  cos2  
## raiz_lgtd       | -0.37  2.21  0.14 | -0.20  1.57  0.04 |  0.66 36.02  0.44 |
## gsr_raiz        | -0.56  5.07  0.32 |  0.75 22.69  0.56 |  0.09  0.60  0.01 |
## num_raiz        |  0.78  9.77  0.61 |  0.02  0.02  0.00 |  0.42 14.44  0.18 |
## peso_fres_raiz  |  0.48  3.61  0.23 | -0.15  0.90  0.02 |  0.70 39.99  0.49 |
## peso_seco_raiz  |  0.64  6.50  0.41 | -0.64 16.61  0.41 |  0.04  0.12  0.00 |
## alt_planta      |  0.95 14.40  0.90 | -0.01  0.00  0.00 | -0.08  0.58  0.01 |
## gsr_tallo       |  0.81 10.40  0.65 |  0.43  7.56  0.19 |  0.10  0.84  0.01 |
## larg_hoja       |  0.92 13.57  0.85 |  0.27  2.99  0.07 | -0.05  0.23  0.00 |
## grs_hoja        | -0.43  2.96  0.19 |  0.76 23.41  0.58 |  0.21  3.53  0.04 |
## anch_hoja       |  0.65  6.77  0.42 |  0.61 14.87  0.37 | -0.05  0.21  0.00 |
## peso_fres_brote |  0.90 12.88  0.81 |  0.41  6.82  0.17 | -0.06  0.25  0.00 |
## peso_seco_brote |  0.86 11.86  0.74 | -0.25  2.56  0.06 | -0.20  3.20  0.04 |
## 
## Supplementary categories
##                    Dist   Dim.1  cos2 v.test   Dim.2  cos2 v.test   Dim.3  cos2
## Creole          |  0.40 | -0.03  0.01  -0.05 |  0.27  0.45   0.62 |  0.02  0.00
## Hybrid          |  0.40 |  0.03  0.01   0.05 | -0.27  0.45  -0.62 | -0.02  0.00
## T0              |  3.24 | -2.70  0.69  -1.59 |  1.58  0.24   1.48 | -0.23  0.00
## T1              |  4.15 |  3.86  0.87   2.27 | -0.42  0.01  -0.39 |  1.27  0.09
## T2              |  3.31 |  2.63  0.63   1.55 |  1.56  0.22   1.46 | -0.37  0.01
## T3              |  2.46 |  1.16  0.22   0.68 | -1.51  0.38  -1.42 | -1.45  0.34
## T4              |  2.24 | -0.71  0.10  -0.42 | -1.68  0.57  -1.58 | -0.41  0.03
## T5              |  2.43 | -1.96  0.65  -1.15 |  1.12  0.21   1.05 |  0.31  0.02
## T6              |  2.79 | -2.30  0.68  -1.35 | -0.65  0.05  -0.61 |  0.87  0.10
##                 v.test  
## Creole            0.08 |
## Hybrid           -0.08 |
## T0               -0.30 |
## T1                1.69 |
## T2               -0.49 |
## T3               -1.92 |
## T4               -0.55 |
## T5                0.41 |
## T6                1.16 |

6.3.16 Figure 3

var <- pca %>% 
  plot.PCA(choix = "var"
           , cex=0.8 
           )

ind <- pca %>% 
  plot.PCA(choix = "ind", habillage = 2
           , label = c("ind")
           , invisible = "quali"
           ) +
  labs(colour = "Treatments") +
  theme(legend.position = "bottom"
        , legend.direction = "horizontal")
  

fig <- list(var, ind) %>% 
    plot_grid(plotlist = .
              , ncol = 2
              , labels = "auto"
              , rel_widths = c(1.5, 2)
              ) 

fig %>% 
  ggsave2(plot = .
          , "files/Fig-3.jpg"
          , units = "cm"
          , width = 30
          , height = 12
         )

fig %>% 
  ggsave2(plot = .
          , "files/Fig-3.eps"
          , units = "cm"
          , width = 30
          , height = 12
         )

include_graphics("files/Fig-3.jpg")